An Optimal Algorithm for Monte Carlo Estimation 论文

摘要

A typical approach to estimate an unknown quantity $\mu$ is to design an experiment that produces a random variable Z, distributed in [0,1] with E[Z]=\mu$, run this experiment independently a number of times, and use the average of the outcomes as the estimate. In this paper, we consider the case when no a priori information about Z is known except that is distributed in [0,1]. We describe an approximation algorithm ${\cal A}{\cal A}$ which, given $\epsilon$ and $\delta$, when running independent experiments with respect to any Z, produces an estimate that is within a factor $1+\epsilon$ of $\mu$ with probability at least $1-\delta$. We prove that the expected number of experiments run by ${\cal A}{\cal A}$ (which depends on Z) is optimal to within a constant factor {for every} Z.